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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/drive/\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
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        "colab": {
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        },
        "id": "NwbIab6lHOzZ",
        "outputId": "06b55ea2-265a-423f-d57a-698b2be75daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[0m\u001b[01;34mdrive\u001b[0m/  \u001b[01;34msample_data\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd \"/content/drive/MyDrive/GAT_USBNAR/CLB\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZnGoYwxZHktH",
        "outputId": "2a579aed-eeaf-4eef-c9e1-d5b0ba5c1bf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/GAT_USBNAR/CLB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "TajkkRKUHlZw",
        "outputId": "95e6e313-54e3-44fe-e357-930f3c4e656e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "annotated_user_da_with_span_full.json            data.json.zip\n",
            "\u001b[0m\u001b[01;34mclb-analysis\u001b[0m/                                    glove.6B.300d.txt\n",
            "CLB_GAT_5_emb_int_n_strategy.csv                 mapping.pair\n",
            "CLB_GAT_5_emb_int_n_strategy_with_sentiment.csv  testListFile.json\n",
            "\u001b[01;34mclb-processed\u001b[0m/                                   turn_lengths.txt\n",
            "CraigListBergain.csv                             valListFile.json\n",
            "data.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import pickle\n",
        "import re\n",
        "import json\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly as plot \n",
        "import plotly.express as px"
      ],
      "metadata": {
        "id": "i9TwtvwHHt55"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"CLB_GAT_5_emb_int_n_strategy_with_sentiment.csv\")\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "-YAV42xYJTC9",
        "outputId": "f24620a1-9eba-469f-a22f-8746199da720"
      },
      "execution_count": null,
      "outputs": [
        {
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              "4                                      eh fine . 7 .   \n",
              "\n",
              "                                               Agent Slot-Value Value  \\\n",
              "0                                                  1          1     1   \n",
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              "2       i think the lowest i would want to go is 8 .        NaN   NaN   \n",
              "3                             7 and we have a deal .        NaN   NaN   \n",
              "4                                                NaN        NaN   NaN   \n",
              "\n",
              "                           Negotiation Strategy User  \\\n",
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              "                          Negotiation Strategy Agent Agent Dialogue Act  \\\n",
              "0                                                  1                  1   \n",
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              "3  ['first_person_plural_count_seller', 'number_o...      counter-price   \n",
              "4                                               ['']                NaN   \n",
              "\n",
              "          Intent                               GAT_n_strategy_agent  \\\n",
              "0              1                                               ['']   \n",
              "1     init-price  ['first_person_singular_count_seller', '-3672'...   \n",
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        "df[['User', 'Agent']] = df[['User', 'Agent']].fillna('')\n",
        "df.head(5)"
      ],
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              "                                                User  \\\n",
              "0                                                  1   \n",
              "1  hi not sure if the charger would work for my c...   \n",
              "2    still can i buy it for 5 ? i m on a tight budge   \n",
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              "                           Negotiation Strategy User  \\\n",
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              "          Intent                               GAT_n_strategy_agent  \\\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9564fe09-47e6-4607-b6d5-0c3493af2d42')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9564fe09-47e6-4607-b6d5-0c3493af2d42 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9564fe09-47e6-4607-b6d5-0c3493af2d42');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(df['User'].isna())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMzBKLVNS6A7",
        "outputId": "58095d0c-972b-4246-87b3-d08a98479eb2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "intent = list(set(list(df['Intent'])))\n",
        "intent = intent[1:]\n",
        "intent = [i for i in intent if not str(i).isdigit()]\n",
        "intent"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AbeUufy-1f1H",
        "outputId": "e13b00bf-0e05-4462-c402-14439b62decc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['init-pricecounter-price',\n",
              " 'agreecounter-price',\n",
              " 'inquiryinquirycounter-price',\n",
              " 'insistunknown',\n",
              " 'unknownunknown',\n",
              " 'unknowndisagree',\n",
              " 'init-priceinsist',\n",
              " 'unknowncounter-price',\n",
              " 'inform',\n",
              " 'inquiry',\n",
              " 'informunknown',\n",
              " 'inquiryinquiry',\n",
              " 'agree',\n",
              " 'vague-price',\n",
              " 'agreeinquiry',\n",
              " 'counter-pricecounter-priceinsist',\n",
              " 'unknowninit-price',\n",
              " 'insist',\n",
              " 'agreeagree',\n",
              " 'counter-priceagree',\n",
              " 'informunknowndisagree',\n",
              " 'inquiryunknowndisagree',\n",
              " 'counter-priceinquiry',\n",
              " 'unknowninsist',\n",
              " 'intro',\n",
              " 'unknownvague-price',\n",
              " 'unknowncounter-priceinsist',\n",
              " 'introagree',\n",
              " 'vague-priceinit-price',\n",
              " 'disagree',\n",
              " 'init-price',\n",
              " 'unknownvague-priceunknown',\n",
              " 'unknownunknownunknown',\n",
              " 'introcounter-price',\n",
              " 'init-priceinquiryinsist',\n",
              " nan,\n",
              " 'counter-pricecounter-price',\n",
              " 'init-priceinit-price',\n",
              " 'inquiryunknown',\n",
              " 'inquiryinquiryinit-price',\n",
              " 'counter-priceunknown',\n",
              " 'introinit-price',\n",
              " 'unknowninquiryinit-priceinsist',\n",
              " 'unknown',\n",
              " 'inquiryinit-price',\n",
              " 'introunknown',\n",
              " 'counter-price',\n",
              " 'inquiryunknowninquiry',\n",
              " 'agreeunknown',\n",
              " 'counter-priceinsist',\n",
              " 'introinit-priceunknown',\n",
              " 'init-priceinquiry',\n",
              " 'introinquiry',\n",
              " 'unknowninquiry',\n",
              " 'inquirycounter-price',\n",
              " 'informinquiry',\n",
              " 'inquirycounter-pricecounter-price',\n",
              " 'unknowninit-priceinsist',\n",
              " 'insistcounter-price',\n",
              " 'introdisagree',\n",
              " 'init-priceunknown',\n",
              " 'vague-priceunknown']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dialog_act = list(set(list(df['Agent Dialogue Act'])))\n",
        "dialog_act = dialog_act[1:]\n",
        "dialog_act = [i for i in dialog_act if not str(i).isdigit()]\n",
        "dialog_act"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fR16BCo56__Y",
        "outputId": "8507c1e4-afc5-42c4-e37e-aab98a3d0061"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['unknownagree',\n",
              " 'init-pricecounter-price',\n",
              " 'unknownunknowncounter-price',\n",
              " 'init-priceagree',\n",
              " 'insistunknown',\n",
              " 'informinforminsist',\n",
              " 'unknownunknown',\n",
              " 'unknowndisagree',\n",
              " 'disagreedisagree',\n",
              " 'unknowncounter-price',\n",
              " 'inform',\n",
              " 'disagreeinsist',\n",
              " 'inquiry',\n",
              " 'unknowndisagreedisagree',\n",
              " 'inquiryagree',\n",
              " 'informunknown',\n",
              " 'inquiryinquiry',\n",
              " 'agree',\n",
              " 'vague-price',\n",
              " 'agreeinquiry',\n",
              " 'informdisagree',\n",
              " 'counter-pricecounter-priceinsist',\n",
              " 'informinit-price',\n",
              " 'unknowninit-price',\n",
              " 'insist',\n",
              " 'counter-pricevague-price',\n",
              " 'counter-priceagree',\n",
              " 'inquirycounter-priceunknownunknown',\n",
              " 'unknownagreeunknown',\n",
              " 'counter-priceinquiry',\n",
              " 'unknowninsist',\n",
              " 'intro',\n",
              " 'unknowndisagreeunknown',\n",
              " 'unknownunknownagree',\n",
              " 'unknownvague-price',\n",
              " 'introagree',\n",
              " 'disagree',\n",
              " 'init-price',\n",
              " 'introcounter-price',\n",
              " nan,\n",
              " 'counter-pricecounter-price',\n",
              " 'informinform',\n",
              " 'counter-pricecounter-pricecounter-pricecounter-price',\n",
              " 'inquiryunknown',\n",
              " 'disagreecounter-price',\n",
              " 'disagreeunknown',\n",
              " 'disagreeinform',\n",
              " 'counter-priceunknown',\n",
              " 'introinit-price',\n",
              " 'unknown',\n",
              " 'insistunknowncounter-price',\n",
              " 'introunknown',\n",
              " 'counter-price',\n",
              " 'disagreeagree',\n",
              " 'agreeunknown',\n",
              " 'informagree',\n",
              " 'counter-priceinsist',\n",
              " 'disagreeinformunknown',\n",
              " 'unknownunknownvague-priceinsist',\n",
              " 'inquiryvague-price',\n",
              " 'informinforminform',\n",
              " 'init-priceinquiry',\n",
              " 'informinsist',\n",
              " 'introinquiry',\n",
              " 'unknowninforminform',\n",
              " 'unknowninquiry',\n",
              " 'informinquiry',\n",
              " 'counter-pricedisagree',\n",
              " 'introdisagree',\n",
              " 'init-priceunknown']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "['init-pricecounter-price','unknownunknownvague-priceinsist','disagree','disagreecounter-price','inquiryinquiry','unknownunknownagree','unknowndisagreedisagree',\n",
        " 'init-priceagree','unknowndisagreeunknown','agree','counter-priceinquiry','unknowninit-price','informinit-price','disagreeinformunknown','introinit-price',\n",
        " 'inquiryunknown','vague-price','counter-pricedisagree','unknownagree','insist','informinquiry','counter-priceunknown','unknowninsist','counter-pricecounter-pricecounter-pricecounter-price',\n",
        " 'introinquiry','informinform','counter-priceinsist','introcounter-price','informunknown','init-price','disagreedisagree','inform','agreeunknown','unknowndisagree',\n",
        " 'inquiryagree','insistunknown','introdisagree','inquiryvague-price','intro','unknownunknowncounter-price','insistunknowncounter-price','counter-pricecounter-price',\n",
        " 'init-priceunknown','counter-pricevague-price','disagreeagree','informagree','unknownunknown','unknownagreeunknown','introunknown','unknowninforminform',\n",
        " 'inquiry','counter-pricecounter-priceinsist','disagreeinform','introagree','agreeinquiry','unknowninquiry','disagreeinsist','counter-priceagree','disagreeunknown',\n",
        " 'informdisagree','unknown','inquirycounter-priceunknownunknown','informinforminform','init-priceinquiry','unknowncounter-price','informinforminsist',\n",
        " 'counter-price','unknownvague-price','informinsist']"
      ],
      "metadata": {
        "id": "wUFrhe2b-Hl-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8e948f8d-30a4-441f-efa3-82b04812a70f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['init-pricecounter-price',\n",
              " 'unknownunknownvague-priceinsist',\n",
              " 'disagree',\n",
              " 'disagreecounter-price',\n",
              " 'inquiryinquiry',\n",
              " 'unknownunknownagree',\n",
              " 'unknowndisagreedisagree',\n",
              " 'init-priceagree',\n",
              " 'unknowndisagreeunknown',\n",
              " 'agree',\n",
              " 'counter-priceinquiry',\n",
              " 'unknowninit-price',\n",
              " 'informinit-price',\n",
              " 'disagreeinformunknown',\n",
              " 'introinit-price',\n",
              " 'inquiryunknown',\n",
              " 'vague-price',\n",
              " 'counter-pricedisagree',\n",
              " 'unknownagree',\n",
              " 'insist',\n",
              " 'informinquiry',\n",
              " 'counter-priceunknown',\n",
              " 'unknowninsist',\n",
              " 'counter-pricecounter-pricecounter-pricecounter-price',\n",
              " 'introinquiry',\n",
              " 'informinform',\n",
              " 'counter-priceinsist',\n",
              " 'introcounter-price',\n",
              " 'informunknown',\n",
              " 'init-price',\n",
              " 'disagreedisagree',\n",
              " 'inform',\n",
              " 'agreeunknown',\n",
              " 'unknowndisagree',\n",
              " 'inquiryagree',\n",
              " 'insistunknown',\n",
              " 'introdisagree',\n",
              " 'inquiryvague-price',\n",
              " 'intro',\n",
              " 'unknownunknowncounter-price',\n",
              " 'insistunknowncounter-price',\n",
              " 'counter-pricecounter-price',\n",
              " 'init-priceunknown',\n",
              " 'counter-pricevague-price',\n",
              " 'disagreeagree',\n",
              " 'informagree',\n",
              " 'unknownunknown',\n",
              " 'unknownagreeunknown',\n",
              " 'introunknown',\n",
              " 'unknowninforminform',\n",
              " 'inquiry',\n",
              " 'counter-pricecounter-priceinsist',\n",
              " 'disagreeinform',\n",
              " 'introagree',\n",
              " 'agreeinquiry',\n",
              " 'unknowninquiry',\n",
              " 'disagreeinsist',\n",
              " 'counter-priceagree',\n",
              " 'disagreeunknown',\n",
              " 'informdisagree',\n",
              " 'unknown',\n",
              " 'inquirycounter-priceunknownunknown',\n",
              " 'informinforminform',\n",
              " 'init-priceinquiry',\n",
              " 'unknowncounter-price',\n",
              " 'informinforminsist',\n",
              " 'counter-price',\n",
              " 'unknownvague-price',\n",
              " 'informinsist']"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "domain = ['common']"
      ],
      "metadata": {
        "id": "mXwGra0k-wa1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_metadata(df):\n",
        "    #should return a json (orient = \"records\")\n",
        "    l = len(df)\n",
        "    d_frame = pd.DataFrame(columns = domain , index = [0])\n",
        "    d_frame = {x:\"\" for x in domain}\n",
        "\n",
        "    # d = str(df[\"Task Info\"]).split(\",\")     # fetching the domain info of that row\n",
        "    # d = [s.strip() for s in d]\n",
        "    d = ['common']\n",
        "\n",
        "    # S = str(df['Slot-Value']).split(\"-\")[0] # fetching the slot info of that row\n",
        "    # s1 = S.split(\",\")\n",
        "    # s1 = [s.strip() for s in s1]\n",
        "    \n",
        "    \n",
        "    s = str(df['Slot-Value']).split(\",\")\n",
        "    s1 = []\n",
        "    for j in range(len(s)):\n",
        "        s1 = s1 + [s[j].split(\"=\")[0]]\n",
        "    s1 = [s.strip() for s in s1]\n",
        "\n",
        "    #print(\"domain :\" ,d)\n",
        "    #print(\"slot:\" ,s1)\n",
        "\n",
        "    for i in domain :\n",
        "        #dic = {x:\"\" for x in dom_slot[i]}\n",
        "        dic = {x:\"\" for x in slot}\n",
        "        if i in d :\n",
        "            value = str(df[\"Value\"]).split(\",\")\n",
        "            k = 0\n",
        "            #print(\"Value :\",value)\n",
        "            for j in s1 :\n",
        "                if j in slot and (str(value[k]) != \"UNK\" and str(value[k]) != \"unk\" and str(value[k]) != \"nan\"):       \n",
        "                    dic[j] = value[k]\n",
        "                    k += 1\n",
        "\n",
        "        d_frame[i] = {\"book\":dict(),\"semi\":dic}\n",
        "\n",
        "       \n",
        "    #return d_frame.to_json().replace('\\\\', '')\n",
        "    return d_frame\n",
        "    \n"
      ],
      "metadata": {
        "id": "fb0P6wwUKUfg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_dialog_act_user(df):\n",
        "    # dom = str(df[\"Task Info\"]).split(\",\")     # fetching the domain info of that row\n",
        "    # dom = [s.strip() for s in dom]\n",
        "\n",
        "    dom = domain\n",
        "\n",
        "    if str(df[\"Value\"]) != 'nan':\n",
        "        for i in range(1):\n",
        "            s = str(df['Slot-Value']).split(\",\") # Fetching the slot info\n",
        "            s1 = []\n",
        "            for j in range(len(s)):\n",
        "                s1 = s1 + [s[j].split(\"=\")[0]]\n",
        "            s1 = [s.strip() for s in s1]\n",
        "\n",
        "        v = str(df[\"Value\"]).split(\",\") # fetching value info\n",
        "        v = [str(vv).strip() for vv in v]\n",
        "\n",
        "        for dd in dom:\n",
        "            d = {dd+\"-\"+str(df['Intent']) : []}\n",
        "\n",
        "            for j in range(len(s1)):\n",
        "                if str(v[j]) != \"UNK\" and str(v[j]) != \"unk\" and str(v[j]) != \"nan\" and str(s1[j]) != \"nan\":\n",
        "                    l = []\n",
        "                    l.append(str(s1[j]))\n",
        "                    l.append(str(v[j]))\n",
        "\n",
        "                    d[list(d.keys())[i]].append(l)\n",
        "    else:\n",
        "        for dd in dom:\n",
        "              d = {dd+\"-\"+str(df['Intent']) : []}\n",
        "              d[list(d.keys())[0]].append([\"None\",\"None\"])                \n",
        "    return d\n",
        "    #return {x:[] for x in str(df['INTENT']).split(',') if str(df['INTENT']) is not None}\n",
        "\n",
        "def make_dialog_act_agent(df):\n",
        "    # dom = str(df[\"Task Info\"]).split(\",\")     # fetching the domain info of that row\n",
        "    # dom = [s.strip() for s in dom]\n",
        "    dom = domain\n",
        "    dic = dict()\n",
        "    for dd in dom:\n",
        "        if str(df['Agent Dialogue Act']) != 'nan':\n",
        "            flag = 0\n",
        "            # print(df[\"Agent\"])\n",
        "            s = df[\"Agent\"].lower()\n",
        "            dic[dd+\"-\"+str(df['Agent Dialogue Act'])] = []\n",
        "            if flag == 0:\n",
        "                if len(re.findall(\"<price>_[|-]*[0-9]*.[0-9]*\",s)) > 0 :\n",
        "                    f = re.findall(\"<price>_[|-]*[0-9]*.[0-9]*\",s)\n",
        "                    ff = f[0][8:].strip()\n",
        "                    dic[dd+\"-\"+str(df['Agent Dialogue Act'])].append([\"Price_ratio\",ff])\n",
        "                    flag = 1\n",
        "            else :\n",
        "                dic[dd+\"-\"+str(df['Agent Dialogue Act'])] = ['none','none']    \n",
        "\n",
        "            \n",
        "    return dic\n",
        "    \n",
        "def make_span_info_user(df):\n",
        "    L = []\n",
        "    if str(df['Intent']) != 'nan':\n",
        "        S = str(df[\"Intent\"])\n",
        "\n",
        "        s = str(df['Slot-Value']).split(\",\") # Fetching the slot info\n",
        "        s1 = []\n",
        "        for j in range(len(s)):\n",
        "            s1 = s1 + [s[j].split(\"=\")[0]]\n",
        "        s1 = [s.strip() for s in s1]\n",
        "\n",
        "        v = str(df[\"Value\"]).split(\",\") # fetching the value info\n",
        "        v = [str(vv).strip() for vv in v]\n",
        "        for i in range(len(v)):\n",
        "            v[i] = str(v[i]).replace(\"-\",\"\")\n",
        "\n",
        "        # dom = str(df[\"Task Info\"]).split(\",\")     # fetching the domain info of that row\n",
        "        # dom = [s.strip() for s in dom]\n",
        "        dom = domain\n",
        "\n",
        "        for dd in dom:    \n",
        "            i = 0\n",
        "            # print(v)\n",
        "            # print(\"domain:\",dd)\n",
        "\n",
        "            for ss in s1:\n",
        "                if str(ss) != \"nan\" :\n",
        "                    # print(\"ss:\",ss)\n",
        "                    l = []\n",
        "\n",
        "                    u = str(df['User'])\n",
        "                    u = u.replace(v[i],\"[place_holder]\")\n",
        "                    u = u.replace(str(v[i]).lower(),\"[place_holder]\")\n",
        "                    u = u.split(\" \")\n",
        "                    # print(u)\n",
        "                    if \"\" in u:\n",
        "                        u.remove(\"\")\n",
        "                    if \" \" in u:    \n",
        "                        u.remove(\" \")\n",
        "                    \n",
        "                    for k in range(len(u)):\n",
        "                        u[k] = u[k].replace(\".\",\"\")\n",
        "                        u[k] = u[k].replace(\",\",\"\")\n",
        "                    # print(u)\n",
        "                    # print(v[i])\n",
        "\n",
        "                    s = [j for j in range(len(u)) if str(u[j]).find('[place_holder]')!=-1]\n",
        "                    \n",
        "                    if len(s)>0 and (str(v[i]) != \"UNK\" and str(v[i]) != \"unk\" and str(v[i]) != \"nan\"):\n",
        "                        l.append(dd+\"-\"+S)\n",
        "                        l.append(str(ss))\n",
        "                        l.append(v[i])\n",
        "                        l.append(s[0])\n",
        "                        l.append(s[0])\n",
        "                    \n",
        "\n",
        "                    i += 1\n",
        "\n",
        "                    if len(l)>0:\n",
        "                        L.append(l)\n",
        "    return L    \n",
        "    #return [[x] for x in str(df['INTENT']).split(',') if str(df['INTENT']) is not None] \n",
        "\n",
        "\n",
        "def make_span_info_agent(df):\n",
        "    # dom = str(df[\"Task Info\"]).split(\",\")     # fetching the domain info of that row\n",
        "    # dom = [s.strip() for s in dom]\n",
        "    dom = domain\n",
        "\n",
        "    L = []\n",
        "    action = df[\"Agent Dialogue Act\"]\n",
        "\n",
        "    if str(action)!=\"nan\":\n",
        "        for dd in dom :\n",
        "            s = df[\"Agent\"].lower()\n",
        "            # print(\"domain:\",dd)\n",
        "            # print(\"action:\",action)\n",
        "            if len(re.findall(\"<price>_[|-]*[0-9]*.[0-9]*\",s)) > 0 :\n",
        "                l = []\n",
        "                f = re.findall(\"<price>_[|-]*[0-9]*.[0-9]*\",s)\n",
        "                ff = ff = f[0][8:].strip()\n",
        "                lim = s.find(ff)\n",
        "                ss = s[:lim]\n",
        "                \n",
        "                l.append(dd+\"-\"+action)\n",
        "                l.append(\"Price_ratio\")\n",
        "                l.append(ff)\n",
        "                l.append(len(ss.split(\" \"))-1)\n",
        "                l.append(len(ss.split(\" \"))-1)\n",
        "\n",
        "                L.append(l)            \n",
        "\n",
        "    #return [[x] for x in str(df['Agent Dialouge Act']).split(',') if str(df['Agent Dialouge Act']) is not None]   \n",
        "    return L        "
      ],
      "metadata": {
        "id": "uE4Lctz6_tMW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_log(df):\n",
        "    # should return a json (orient = \"records\")\n",
        "    l = len(df)\n",
        "    # text = []\n",
        "    # metadata = []\n",
        "\n",
        "    ll = []\n",
        "\n",
        "    for i in range(2*l):\n",
        "        k = i//2\n",
        "        \n",
        "        if i%2 == 0 : # user's turn\n",
        "            dframe = {\"text\":\"\",\"metadata\":\"\"}\n",
        "            if str(df.iloc[k,3]) != \"nan\" :\n",
        "                dframe[\"text\"] = df.iloc[k,0] #+ \" \" +  \"<eos_u>\" + \" \" + \"<sos_s>\" + \" \" + df.iloc[k,3] + \" \" + \"<eos_s>\"\n",
        "            else :  \n",
        "                dframe[\"text\"] = df.iloc[k,0] \n",
        "            # dframe[\"sentiment\"] = df.iloc[k,3]\n",
        "            \n",
        "\n",
        "            dframe[\"metadata\"] = dict()\n",
        "            dframe['dialog_act'] = make_dialog_act_user(df.iloc[k]) # for annotated_user_da_with_span.json file\n",
        "            dframe['span_info'] = make_span_info_user(df.iloc[k])     # for annotated_user_da_with_span.json file\n",
        "\n",
        "            #################### IF WANT TO ADD NEGOTIATION STRATEGY INFO INTO THE CONTEXT THEN ACTIVATE THE BELOW LINE ####################\n",
        "            dframe[\"n_strategy\"] = df.iloc[k,4]\n",
        "\n",
        "        else : # agent's turn\n",
        "            dframe = {\"text\":\"\",\"metadata\":\"\"}\n",
        "            dframe[\"text\"] = df.iloc[k,1] #\"<sos_ps>\" + \" \" + df.iloc[k,2] + \" \" + \"<eos_ps>\" + \" \" + \"<sos_r>\" + \" \" + df.iloc[k,1]\n",
        "\n",
        "            #################### IF WANT TO ADD NEGOTIATION STRATEGY INFO INTO THE CONTEXT THEN ACTIVATE THE BELOW LINE ####################\n",
        "            dframe[\"n_strategy\"] = df.iloc[k,8]\n",
        "            #################### IF WANT TO ADD SENTIMENT INFO INTO THE CONTEXT THEN ACTIVATE THE BELOW LINE ####################\n",
        "            dframe[\"sentiment\"] = df.iloc[k,9]\n",
        "\n",
        "            dframe[\"metadata\"] = make_metadata(df.iloc[k])\n",
        "            dframe['dialog_act'] = make_dialog_act_agent(df.iloc[k])# for annotated_user_da_with_span.json file\n",
        "            dframe['span_info'] = make_span_info_agent(df.iloc[k])    # for annotated_user_da_with_span.json file\n",
        "\n",
        "        ll.append(dframe)      \n",
        "        \n",
        "    #dframe = pd.DataFrame(columns=[\"metadata\",\"text\"])\n",
        "    \n",
        "    \n",
        "    #return dframe.to_json(orient = \"records\").replace(\"\\\\\",\"\")\n",
        "    return ll\n"
      ],
      "metadata": {
        "id": "nXr-7QqhCcWr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_goal(df):\n",
        "    df.index = [i for i in range(len(df))]\n",
        "    # should return a json (orient = \"records\")\n",
        "    l = len(df)\n",
        "    # print(df[\"Task Info\"][0])\n",
        "    dic = {x:dict() for x in domain}\n",
        "    \n",
        "    all_dom = []\n",
        "    for i in range(l):\n",
        "        # d = str(df[\"Task Info\"][i]).split(\",\")     # fetching the domain info of that row\n",
        "        # d = [s.strip() for s in d]\n",
        "        d = domain\n",
        "        all_dom = all_dom + d\n",
        "    all_dom = list(set(all_dom))\n",
        "    if 'nan' in all_dom:\n",
        "        all_dom.remove('nan')\n",
        "    \n",
        "    for i in all_dom:\n",
        "        dict1 = {x:\"\" for x in slot}\n",
        "        dic[i] = {\"info\": dict1}\n",
        "\n",
        "    for i in range(l):\n",
        "        # d = str(df[\"Task Info\"][i]).split(\",\")     # fetching the domain info of that row\n",
        "        # d = [s.strip() for s in d]\n",
        "        d = domain\n",
        "        #d.remove(\"nan\")\n",
        "        #print(d)\n",
        "\n",
        "        s = str(df['Slot-Value'][i]).split(\",\")\n",
        "        s1 = []\n",
        "        for j in range(len(s)):\n",
        "            s1 = s1 + [s[j].split(\"=\")[0]]\n",
        "        s1 = [s.strip() for s in s1]\n",
        "        #print(s1)\n",
        "        value = str(df[\"Value\"][i]).split(\",\")\n",
        "        #print(value)\n",
        "        # print(\"len s1:\",s1)\n",
        "        # print(\"len value:\",value)\n",
        "        # domain_dict = {y:\"\" for y in slot}\n",
        "        for dd in d:\n",
        "            for j in range(len(s1)):\n",
        "                # print(j)\n",
        "                if dd in all_dom and str(value[j])!=\"UNK\" and str(value[j]) != \"unk\" and str(value[j]) != \"nan\" and str(s1[j]) != \"nan\":\n",
        "                    # print(\"value:\",value[j])\n",
        "                    # print(\"slot:\",s1[j])\n",
        "                    dic[dd][\"info\"][s1[j]] = value[j]\n",
        "                    #domain_dict[s1[j]] = value[j]\n",
        "\n",
        "            #dic[dd] = {\"info\":domain_dict }   \n",
        "\n",
        "\n",
        "    return dic"
      ],
      "metadata": {
        "id": "0FUV9HcpEIFJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def maindata(df):\n",
        "    #main_df = pd.DataFrame(index = [0])\n",
        "    turn_len = []\n",
        "    main_df = dict()\n",
        "    #main_df.index = [\"goal\",\"log\"]\n",
        "    i = 1\n",
        "    lc = 0\n",
        "    ll = 0\n",
        "    while lc < len(df):\n",
        "        #print(\"hello\")\n",
        "        flag = 0\n",
        "        ul = len(df)\n",
        "        if str(df.iloc[lc,0]) == str(i) or str(df.iloc[lc,0]).isdigit():\n",
        "            #print(\"hello\")\n",
        "            i += 1\n",
        "            ll = lc + 1\n",
        "            \n",
        "            k = ll\n",
        "            while k < len(df) and flag == 0:\n",
        "                if str(df.iloc[k,0]) == str(i) or str(df.iloc[k,0]).isdigit():\n",
        "                    ul = k\n",
        "                    lc = ul-1\n",
        "                    flag = 1\n",
        "        \n",
        "                k += 1 \n",
        "\n",
        "            print(\"lower limit : \",ll)\n",
        "            print(\"upper limit : \",ul) \n",
        "\n",
        "            turn_len.append((ul-ll)-1)\n",
        "\n",
        "            goal = make_goal(df[ll:ul])\n",
        "            log = make_log(df[ll:ul])\n",
        "            main_df[str(i-1)+\".json\"] = {\"goal\":goal,\"log\":log}\n",
        "              \n",
        "        lc += 1 \n",
        "    return main_df,turn_len\n"
      ],
      "metadata": {
        "id": "-xkgdbzXEbE3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "l = len(df)\n",
        "f_slot_list = []\n",
        "for i in range(l):\n",
        "    s = str(df[\"Slot-Value\"][i]).split(\",\")\n",
        "    s = [s[j].split(\"=\") for j in range(len(s))]\n",
        "    s1 = []\n",
        "    for j in range(len(s)):\n",
        "        s1 = s1+s[j]\n",
        "    for j in range(len(s1)):\n",
        "        f_slot_list.append(s1[::2][0])\n",
        "\n",
        "f_slot_list = list(set(f_slot_list))\n",
        "f_slot_list = [f_slot_list[i].strip() for i in range(len(f_slot_list))]\n",
        "slot = list(set(sorted(f_slot_list)))\n",
        "\n",
        "slot.remove(\"\")\n",
        "slot.remove(\"nan\")\n",
        "\n",
        "slot = [i for i in slot if not str(i).isdigit()]\n"
      ],
      "metadata": {
        "id": "Q3fc65l2Eetm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "slot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kPzY7B-DEm8c",
        "outputId": "d4ba454d-a1ca-4aaa-884a-311e36cc8793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Price_ratio']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f,turn_len = maindata(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1hRdvxOLEodI",
        "outputId": "8994fa70-3bf6-46c3-88f9-40c4670db49e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "lower limit :  18832\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "json_object = json.dumps(f, indent = 4)\n",
        "with open(\"data.json\", \"w\") as outfile:\n",
        "    outfile.write(json_object)"
      ],
      "metadata": {
        "id": "S1ajsJ8hFD_J"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "json_object = json.dumps(f, indent = 4)\n",
        "with open(\"annotated_user_da_with_span_full.json\", \"w\") as outfile:\n",
        "    outfile.write(json_object)"
      ],
      "metadata": {
        "id": "dfv2iDhuFOIV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"turn_lengths.txt\", \"w\") as outfile:\n",
        "    outfile.write(str(turn_len[:-1]))"
      ],
      "metadata": {
        "id": "-6_ws_3_uYRP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Turn-lengths visualization :::"
      ],
      "metadata": {
        "id": "ihM2fnixvdWW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('turn_lengths.txt') as f:\n",
        "    lines = f.readlines()\n",
        "\n",
        "l = lines[0].split(\", \")\n",
        "l[0] = l[0].split(\"[\")[1]\n",
        "l[-1] = l[-1].split(\"]\")[0]\n",
        "\n",
        "d = pd.Series(l).value_counts()\n",
        "ld = d.index\n",
        "ld = [int(i) for i in ld]\n",
        "data = pd.DataFrame({'indices': ld, 'value': list(d.values)})\n",
        "data = data.sort_values(by='indices')\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "tgHkyxEivHmk",
        "outputId": "8fdaa35b-05e9-4712-e674-9180108e1503"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    indices  value\n",
              "8         0     56\n",
              "4         1    393\n",
              "2         2   1026\n",
              "0         3   1403\n",
              "1         4   1239\n",
              "3         5    731\n",
              "5         6    320\n",
              "6         7    136\n",
              "7         8     57\n",
              "9         9     21\n",
              "10       10      3\n",
              "13       12      1\n",
              "12       13      1\n",
              "11       21      1"
            ],
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              "      <td>731</td>\n",
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              "      <td>21</td>\n",
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              "      <th>13</th>\n",
              "      <td>12</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f1c4205-dc89-414f-80e2-7ccaa3f5f725')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f1c4205-dc89-414f-80e2-7ccaa3f5f725 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f1c4205-dc89-414f-80e2-7ccaa3f5f725');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize = ( 15 , 8 ))\n",
        "sns.barplot(ax=ax, x=data.indices, y=data.value)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 514
        },
        "id": "RpPeU-OO4PPm",
        "outputId": "48bc4af8-43e2-4dc0-c6b3-ba5d7d38fffd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f11c2d23210>"
            ]
          },
          "metadata": {},
          "execution_count": 132
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creating valListFile.json and testListFile.json :::"
      ],
      "metadata": {
        "id": "LI_Qg-BAXHSS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cd \"../Nego_UBAR/data\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pKNRAQloV2bT",
        "outputId": "a3e60248-aad0-4107-cb30-e6b81bfda547"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/Nego_UBAR/data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BorH7MpXWAwP",
        "outputId": "fd3c4ad1-6a40-483f-8954-eb373c2568fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "annotated_user_da_with_span_full.json  data.zip           valListFile.json\n",
            "CraigListBergain.csv                   glove.6B.300d.txt\n",
            "data.json                              testListFile.json\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import zipfile\n",
        "archive = zipfile.ZipFile('data.zip', 'r')\n",
        "data = archive.open('data.json', 'r').read()\n",
        "data = json.loads(data)"
      ],
      "metadata": {
        "id": "eG33Q0PiLhb-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(5956 - ((5956*75)//100))//2 + ((5956*75)//100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJlIvvmOemFy",
        "outputId": "6a0279ae-2700-4116-aa0b-3c26a26ed8e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5211"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f = open('valListFile.json','w')\n",
        "f.close()\n",
        "\n",
        "f1 = open('testListFile.json','w')\n",
        "f1.close()\n",
        "\n",
        "flag = 0\n",
        "\n",
        "for fn, dial in data.items():\n",
        "    if fn == '4467.json':\n",
        "        flag = 1\n",
        "    elif fn == '5211.json' :\n",
        "        flag = 2\n",
        "\n",
        "    if flag == 1 :\n",
        "        f = open('valListFile.json','a')\n",
        "        f.write(str(fn))\n",
        "        f.write('\\n') \n",
        "        f.close()\n",
        "    elif flag == 2 :\n",
        "        f1 = open('testListFile.json','a')\n",
        "        f1.write(str(fn))\n",
        "        f1.write('\\n')\n",
        "        f1.close()     \n",
        "    print(fn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LCcBzXzfT_DX",
        "outputId": "390d8d51-d1dc-4b6d-e1a2-228613ba71ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "izPXW-rEVEZj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}